Molecule discovery serves as a cornerstone in numerous scientific domains,
fueling the development of new materials and innovative drug designs. Recent
developments of in-silico molecule discovery have highlighted the promising
results of cross-modal techniques, which bridge molecular structures with their
descriptive annotations. However, these cross-modal methods frequently
encounter the issue of data scarcity, hampering their performance and
application. In this paper, we address the low-resource challenge by utilizing
artificially-real data generated by Large Language Models (LLMs). We first
introduce a retrieval-based prompting strategy to construct high-quality pseudo
data, then explore the optimal method to effectively leverage this pseudo data.
Experiments show that using pseudo data for domain adaptation outperforms all
existing methods, while also requiring a smaller model scale, reduced data size
and lower training cost, highlighting its efficiency. Furthermore, our method
shows a sustained improvement as the volume of pseudo data increases, revealing
the great potential of pseudo data in advancing low-resource cross-modal
molecule discovery.

利用大型语言模型生成的人工真实数据，我们通过提出一种查询引导策略以构建高质量的伪数据，并探索了有效利用这些伪数据的最佳方法来解决低资源挑战。实验证明，使用伪数据进行领域适应的效果优于所有现有的方法，同时需要更小的模型规模、更少的数据量和更低的训练成本，凸显其高效性。此外，我们的方法在伪数据量增加时持续改进，揭示了伪数据在推进低资源跨模态分子发现方面的巨大潜力。

从人工制造到真实：利用大型语言模型的伪数据进行低资源分子发现

From Artificially Real to Real: Leveraging Pseudo Data from Large  Language Models for Low-Resource Molecule Discovery

Human activity recognition (HAR) is a time series classification task that
focuses on identifying the motion patterns from human sensor readings. Adequate
data is essential but a major bottleneck for training a generalizable HAR
model, which assists customization and optimization of online web applications.
However, it is costly in time and economy to collect large-scale labeled data
in reality, i.e., the low-resource challenge. Meanwhile, data collected from
different persons have distribution shifts due to different living habits, body
shapes, age groups, etc. The low-resource and distribution shift challenges are
detrimental to HAR when applying the trained model to new unseen subjects. In
this paper, we propose a novel approach called Diverse and Discriminative
representation Learning (DDLearn) for generalizable low-resource HAR. DDLearn
simultaneously considers diversity and discrimination learning. With the
constructed self-supervised learning task, DDLearn enlarges the data diversity
and explores the latent activity properties. Then, we propose a diversity
preservation module to preserve the diversity of learned features by enlarging
the distribution divergence between the original and augmented domains.
Meanwhile, DDLearn also enhances semantic discrimination by learning
discriminative representations with supervised contrastive learning. Extensive
experiments on three public HAR datasets demonstrate that our method
significantly outperforms state-of-art methods by an average accuracy
improvement of 9.5% under the low-resource distribution shift scenarios, while
being a generic, explainable, and flexible framework.

提出了一种名为 DDLearn 的新方法，通过构建自监督学习任务，同时考虑多样性和判别性学习，扩大数据多样性和探索潜在的活动属性，从而实现针对低资源人类活动识别的泛化。